Overview

Dataset statistics

Number of variables26
Number of observations5000
Missing cells797
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory657.1 B

Variable types

Text1
Numeric17
Categorical7
Boolean1

Alerts

age is highly overall correlated with annual_income and 1 other fieldsHigh correlation
annual_income is highly overall correlated with age and 1 other fieldsHigh correlation
credit_score is highly overall correlated with age and 1 other fieldsHigh correlation
shopping_frequency_monthly is highly overall correlated with monthly_spendingHigh correlation
avg_basket_size is highly overall correlated with monthly_spendingHigh correlation
monthly_spending is highly overall correlated with shopping_frequency_monthly and 1 other fieldsHigh correlation
city is highly overall correlated with provinceHigh correlation
province is highly overall correlated with cityHigh correlation
annual_income has 154 (3.1%) missing valuesMissing
customer_satisfaction has 391 (7.8%) missing valuesMissing
organic_preference_pct has 252 (5.0%) missing valuesMissing
customer_id has unique valuesUnique
organic_preference_pct has 238 (4.8%) zerosZeros

Reproduction

Analysis started2025-08-12 12:52:16.809749
Analysis finished2025-08-12 12:52:52.824890
Duration36.02 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

customer_id
Text

UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.2 KiB
2025-08-12T08:52:53.075151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters55000
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st rowCUST_000001
2nd rowCUST_000002
3rd rowCUST_000003
4th rowCUST_000004
5th rowCUST_000005
ValueCountFrequency (%)
cust_000001 1
 
< 0.1%
cust_000012 1
 
< 0.1%
cust_000005 1
 
< 0.1%
cust_000006 1
 
< 0.1%
cust_000007 1
 
< 0.1%
cust_000008 1
 
< 0.1%
cust_000009 1
 
< 0.1%
cust_000038 1
 
< 0.1%
cust_000010 1
 
< 0.1%
cust_000013 1
 
< 0.1%
Other values (4990) 4990
99.8%
2025-08-12T08:52:53.423955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12499
22.7%
C 5000
9.1%
U 5000
9.1%
S 5000
9.1%
T 5000
9.1%
_ 5000
9.1%
1 2500
 
4.5%
3 2500
 
4.5%
4 2500
 
4.5%
2 2500
 
4.5%
Other values (5) 7501
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30000
54.5%
Uppercase Letter 20000
36.4%
Connector Punctuation 5000
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12499
41.7%
1 2500
 
8.3%
3 2500
 
8.3%
4 2500
 
8.3%
2 2500
 
8.3%
5 1501
 
5.0%
8 1500
 
5.0%
7 1500
 
5.0%
6 1500
 
5.0%
9 1500
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
C 5000
25.0%
U 5000
25.0%
S 5000
25.0%
T 5000
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35000
63.6%
Latin 20000
36.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12499
35.7%
_ 5000
14.3%
1 2500
 
7.1%
3 2500
 
7.1%
4 2500
 
7.1%
2 2500
 
7.1%
5 1501
 
4.3%
8 1500
 
4.3%
7 1500
 
4.3%
6 1500
 
4.3%
Latin
ValueCountFrequency (%)
C 5000
25.0%
U 5000
25.0%
S 5000
25.0%
T 5000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12499
22.7%
C 5000
9.1%
U 5000
9.1%
S 5000
9.1%
T 5000
9.1%
_ 5000
9.1%
1 2500
 
4.5%
3 2500
 
4.5%
4 2500
 
4.5%
2 2500
 
4.5%
Other values (5) 7501
13.6%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.778
Minimum18
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:53.552644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q135
median45
Q354
95-th percentile69
Maximum80
Range62
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.288553
Coefficient of variation (CV)0.31909762
Kurtosis-0.48924733
Mean44.778
Median Absolute Deviation (MAD)10
Skewness0.11712857
Sum223890
Variance204.16275
MonotonicityNot monotonic
2025-08-12T08:52:53.660504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 209
 
4.2%
52 143
 
2.9%
44 143
 
2.9%
48 142
 
2.8%
41 139
 
2.8%
47 138
 
2.8%
49 135
 
2.7%
42 126
 
2.5%
43 125
 
2.5%
37 125
 
2.5%
Other values (53) 3575
71.5%
ValueCountFrequency (%)
18 209
4.2%
19 22
 
0.4%
20 30
 
0.6%
21 46
 
0.9%
22 40
 
0.8%
23 43
 
0.9%
24 63
 
1.3%
25 48
 
1.0%
26 56
 
1.1%
27 62
 
1.2%
ValueCountFrequency (%)
80 45
0.9%
79 11
 
0.2%
78 5
 
0.1%
77 15
 
0.3%
76 18
 
0.4%
75 18
 
0.4%
74 17
 
0.3%
73 22
0.4%
72 25
0.5%
71 26
0.5%

gender
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size302.9 KiB
Female
2492 
Male
2435 
Other
 
73

Length

Max length6
Median length5
Mean length5.0114
Min length4

Characters and Unicode

Total characters25057
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 2492
49.8%
Male 2435
48.7%
Other 73
 
1.5%

Length

2025-08-12T08:52:53.763410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T08:52:53.870361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
female 2492
49.8%
male 2435
48.7%
other 73
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 7492
29.9%
a 4927
19.7%
l 4927
19.7%
F 2492
 
9.9%
m 2492
 
9.9%
M 2435
 
9.7%
O 73
 
0.3%
t 73
 
0.3%
h 73
 
0.3%
r 73
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20057
80.0%
Uppercase Letter 5000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7492
37.4%
a 4927
24.6%
l 4927
24.6%
m 2492
 
12.4%
t 73
 
0.4%
h 73
 
0.4%
r 73
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
F 2492
49.8%
M 2435
48.7%
O 73
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 25057
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7492
29.9%
a 4927
19.7%
l 4927
19.7%
F 2492
 
9.9%
m 2492
 
9.9%
M 2435
 
9.7%
O 73
 
0.3%
t 73
 
0.3%
h 73
 
0.3%
r 73
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25057
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7492
29.9%
a 4927
19.7%
l 4927
19.7%
F 2492
 
9.9%
m 2492
 
9.9%
M 2435
 
9.7%
O 73
 
0.3%
t 73
 
0.3%
h 73
 
0.3%
r 73
 
0.3%

annual_income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4673
Distinct (%)96.4%
Missing154
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean67179.537
Minimum25000
Maximum146251.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:54.087209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25000
5-th percentile29270.765
Q151395.905
median66368.45
Q382702.86
95-th percentile104291.49
Maximum146251.87
Range121251.87
Interquartile range (IQR)31306.955

Descriptive statistics

Standard deviation22337.728
Coefficient of variation (CV)0.33250791
Kurtosis-0.32916262
Mean67179.537
Median Absolute Deviation (MAD)15588.39
Skewness0.18829413
Sum3.2555204 × 108
Variance4.9897408 × 108
MonotonicityNot monotonic
2025-08-12T08:52:54.196638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25000 173
 
3.5%
54024.47 2
 
< 0.1%
53830.66 1
 
< 0.1%
77963.34 1
 
< 0.1%
90522.32 1
 
< 0.1%
67183 1
 
< 0.1%
94531.72 1
 
< 0.1%
35713.4 1
 
< 0.1%
71666.22 1
 
< 0.1%
75488.64 1
 
< 0.1%
Other values (4663) 4663
93.3%
(Missing) 154
 
3.1%
ValueCountFrequency (%)
25000 173
3.5%
25055.41 1
 
< 0.1%
25108.38 1
 
< 0.1%
25125.36 1
 
< 0.1%
25152.21 1
 
< 0.1%
25179.94 1
 
< 0.1%
25219.51 1
 
< 0.1%
25260.81 1
 
< 0.1%
25293.7 1
 
< 0.1%
25296.99 1
 
< 0.1%
ValueCountFrequency (%)
146251.87 1
< 0.1%
139155.22 1
< 0.1%
137571.74 1
< 0.1%
137567.47 1
< 0.1%
136205.84 1
< 0.1%
135271.09 1
< 0.1%
135260.33 1
< 0.1%
134168.99 1
< 0.1%
133906.11 1
< 0.1%
133482.36 1
< 0.1%

credit_score
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean685.342
Minimum442
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:54.306389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum442
5-th percentile566.95
Q1633.75
median685
Q3736
95-th percentile808.05
Maximum850
Range408
Interquartile range (IQR)102.25

Descriptive statistics

Standard deviation73.478933
Coefficient of variation (CV)0.10721499
Kurtosis-0.32736025
Mean685.342
Median Absolute Deviation (MAD)51
Skewness0.0014889287
Sum3426710
Variance5399.1537
MonotonicityNot monotonic
2025-08-12T08:52:54.411928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 79
 
1.6%
698 39
 
0.8%
639 36
 
0.7%
688 35
 
0.7%
694 34
 
0.7%
687 34
 
0.7%
650 33
 
0.7%
713 33
 
0.7%
699 33
 
0.7%
677 33
 
0.7%
Other values (351) 4611
92.2%
ValueCountFrequency (%)
442 1
< 0.1%
451 1
< 0.1%
452 1
< 0.1%
462 1
< 0.1%
470 1
< 0.1%
473 1
< 0.1%
480 2
< 0.1%
486 1
< 0.1%
487 1
< 0.1%
488 1
< 0.1%
ValueCountFrequency (%)
850 79
1.6%
849 4
 
0.1%
848 1
 
< 0.1%
847 2
 
< 0.1%
846 5
 
0.1%
845 3
 
0.1%
844 4
 
0.1%
843 2
 
< 0.1%
842 1
 
< 0.1%
841 2
 
< 0.1%

city
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size318.0 KiB
Kitchener
536 
Winnipeg
527 
Edmonton
525 
Ottawa
515 
Hamilton
511 
Other values (5)
2386 

Length

Max length11
Median length9
Mean length8.1074
Min length6

Characters and Unicode

Total characters40537
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEdmonton
2nd rowHamilton
3rd rowMontreal
4th rowCalgary
5th rowWinnipeg

Common Values

ValueCountFrequency (%)
Kitchener 536
10.7%
Winnipeg 527
10.5%
Edmonton 525
10.5%
Ottawa 515
10.3%
Hamilton 511
10.2%
Montreal 489
9.8%
Quebec City 489
9.8%
Vancouver 486
9.7%
Calgary 469
9.4%
Toronto 453
9.1%

Length

2025-08-12T08:52:54.512452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T08:52:54.613670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
kitchener 536
9.8%
winnipeg 527
9.6%
edmonton 525
9.6%
ottawa 515
9.4%
hamilton 511
9.3%
montreal 489
8.9%
quebec 489
8.9%
city 489
8.9%
vancouver 486
8.9%
calgary 469
8.5%

Most occurring characters

ValueCountFrequency (%)
n 4579
 
11.3%
t 4033
 
9.9%
o 3895
 
9.6%
e 3552
 
8.8%
a 3454
 
8.5%
i 2590
 
6.4%
r 2433
 
6.0%
c 1511
 
3.7%
l 1469
 
3.6%
m 1036
 
2.6%
Other values (20) 11985
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 34559
85.3%
Uppercase Letter 5489
 
13.5%
Space Separator 489
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 4579
13.2%
t 4033
11.7%
o 3895
11.3%
e 3552
10.3%
a 3454
10.0%
i 2590
7.5%
r 2433
7.0%
c 1511
 
4.4%
l 1469
 
4.3%
m 1036
 
3.0%
Other values (9) 6007
17.4%
Uppercase Letter
ValueCountFrequency (%)
C 958
17.5%
K 536
9.8%
W 527
9.6%
E 525
9.6%
O 515
9.4%
H 511
9.3%
Q 489
8.9%
M 489
8.9%
V 486
8.9%
T 453
8.3%
Space Separator
ValueCountFrequency (%)
489
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40048
98.8%
Common 489
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 4579
 
11.4%
t 4033
 
10.1%
o 3895
 
9.7%
e 3552
 
8.9%
a 3454
 
8.6%
i 2590
 
6.5%
r 2433
 
6.1%
c 1511
 
3.8%
l 1469
 
3.7%
m 1036
 
2.6%
Other values (19) 11496
28.7%
Common
ValueCountFrequency (%)
489
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 4579
 
11.3%
t 4033
 
9.9%
o 3895
 
9.6%
e 3552
 
8.8%
a 3454
 
8.5%
i 2590
 
6.4%
r 2433
 
6.0%
c 1511
 
3.7%
l 1469
 
3.6%
m 1036
 
2.6%
Other values (20) 11985
29.6%

province
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size288.2 KiB
ON
2015 
AB
994 
QC
978 
MB
527 
BC
486 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10000
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAB
2nd rowON
3rd rowQC
4th rowAB
5th rowMB

Common Values

ValueCountFrequency (%)
ON 2015
40.3%
AB 994
19.9%
QC 978
19.6%
MB 527
 
10.5%
BC 486
 
9.7%

Length

2025-08-12T08:52:54.724150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T08:52:54.808196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
on 2015
40.3%
ab 994
19.9%
qc 978
19.6%
mb 527
 
10.5%
bc 486
 
9.7%

Most occurring characters

ValueCountFrequency (%)
O 2015
20.2%
N 2015
20.2%
B 2007
20.1%
C 1464
14.6%
A 994
9.9%
Q 978
9.8%
M 527
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2015
20.2%
N 2015
20.2%
B 2007
20.1%
C 1464
14.6%
A 994
9.9%
Q 978
9.8%
M 527
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2015
20.2%
N 2015
20.2%
B 2007
20.1%
C 1464
14.6%
A 994
9.9%
Q 978
9.8%
M 527
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 2015
20.2%
N 2015
20.2%
B 2007
20.1%
C 1464
14.6%
A 994
9.9%
Q 978
9.8%
M 527
 
5.3%

household_size
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8068
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:54.881295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2484332
Coefficient of variation (CV)0.4447888
Kurtosis-0.51913454
Mean2.8068
Median Absolute Deviation (MAD)1
Skewness0.37929566
Sum14034
Variance1.5585855
MonotonicityNot monotonic
2025-08-12T08:52:54.952113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1503
30.1%
3 1241
24.8%
4 1023
20.5%
1 764
15.3%
5 365
 
7.3%
6 104
 
2.1%
ValueCountFrequency (%)
1 764
15.3%
2 1503
30.1%
3 1241
24.8%
4 1023
20.5%
5 365
 
7.3%
6 104
 
2.1%
ValueCountFrequency (%)
6 104
 
2.1%
5 365
 
7.3%
4 1023
20.5%
3 1241
24.8%
2 1503
30.1%
1 764
15.3%

shopping_frequency_monthly
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0286
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:55.030132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q310
95-th percentile13
Maximum20
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8592686
Coefficient of variation (CV)0.3561354
Kurtosis0.046040051
Mean8.0286
Median Absolute Deviation (MAD)2
Skewness0.34304308
Sum40143
Variance8.1754171
MonotonicityNot monotonic
2025-08-12T08:52:55.110255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8 742
14.8%
7 675
13.5%
9 601
12.0%
6 572
11.4%
5 494
9.9%
10 478
9.6%
11 350
7.0%
4 267
 
5.3%
12 265
 
5.3%
13 151
 
3.0%
Other values (10) 405
8.1%
ValueCountFrequency (%)
1 16
 
0.3%
2 59
 
1.2%
3 144
 
2.9%
4 267
 
5.3%
5 494
9.9%
6 572
11.4%
7 675
13.5%
8 742
14.8%
9 601
12.0%
10 478
9.6%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 3
 
0.1%
18 4
 
0.1%
17 9
 
0.2%
16 19
 
0.4%
15 58
 
1.2%
14 92
 
1.8%
13 151
3.0%
12 265
5.3%
11 350
7.0%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct3354
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.015062
Minimum10
Maximum110.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:55.212713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28.2935
Q144.0775
median54.66
Q366.01
95-th percentile81.732
Maximum110.78
Range100.78
Interquartile range (IQR)21.9325

Descriptive statistics

Standard deviation16.30695
Coefficient of variation (CV)0.29640882
Kurtosis-0.033681737
Mean55.015062
Median Absolute Deviation (MAD)11.035
Skewness0.039762208
Sum275075.31
Variance265.91661
MonotonicityNot monotonic
2025-08-12T08:52:55.316509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 16
 
0.3%
56.27 6
 
0.1%
46.55 6
 
0.1%
45.24 5
 
0.1%
49.15 5
 
0.1%
63.57 5
 
0.1%
47.31 5
 
0.1%
54.13 5
 
0.1%
50.55 5
 
0.1%
49.67 5
 
0.1%
Other values (3344) 4937
98.7%
ValueCountFrequency (%)
10 16
0.3%
10.36 1
 
< 0.1%
10.64 1
 
< 0.1%
10.87 1
 
< 0.1%
11.45 1
 
< 0.1%
11.96 1
 
< 0.1%
11.99 1
 
< 0.1%
12.02 1
 
< 0.1%
12.21 1
 
< 0.1%
12.39 1
 
< 0.1%
ValueCountFrequency (%)
110.78 1
< 0.1%
110.26 1
< 0.1%
108.52 1
< 0.1%
108.38 1
< 0.1%
107.57 1
< 0.1%
106.47 1
< 0.1%
106.06 1
< 0.1%
105.38 1
< 0.1%
104.89 1
< 0.1%
104.44 1
< 0.1%

monthly_spending
Real number (ℝ)

HIGH CORRELATION 

Distinct4846
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441.21369
Minimum21.44
Maximum1709.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:55.422071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.44
5-th percentile148.6525
Q1285.9075
median408.465
Q3562.2975
95-th percentile841.65
Maximum1709.92
Range1688.48
Interquartile range (IQR)276.39

Descriptive statistics

Standard deviation216.18315
Coefficient of variation (CV)0.4899738
Kurtosis1.2316142
Mean441.21369
Median Absolute Deviation (MAD)136.18
Skewness0.91497144
Sum2206068.4
Variance46735.153
MonotonicityNot monotonic
2025-08-12T08:52:55.527060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205.04 3
 
0.1%
624.55 2
 
< 0.1%
339.17 2
 
< 0.1%
442.61 2
 
< 0.1%
343.45 2
 
< 0.1%
694.68 2
 
< 0.1%
374.33 2
 
< 0.1%
409.08 2
 
< 0.1%
315.83 2
 
< 0.1%
432.97 2
 
< 0.1%
Other values (4836) 4979
99.6%
ValueCountFrequency (%)
21.44 1
< 0.1%
26.86 1
< 0.1%
27.46 1
< 0.1%
29.07 1
< 0.1%
32.42 1
< 0.1%
33.61 1
< 0.1%
38.91 1
< 0.1%
42.31 1
< 0.1%
42.91 1
< 0.1%
43.2 1
< 0.1%
ValueCountFrequency (%)
1709.92 1
< 0.1%
1490.07 1
< 0.1%
1476.18 1
< 0.1%
1459.2 1
< 0.1%
1456.7 1
< 0.1%
1431.53 1
< 0.1%
1431.02 1
< 0.1%
1368.76 1
< 0.1%
1274.48 1
< 0.1%
1261.24 1
< 0.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size341.7 KiB
Supermarket
1998 
Discount Store
1259 
Organic Store
772 
Convenience Store
505 
Warehouse Club
466 

Length

Max length17
Median length14
Mean length12.9498
Min length11

Characters and Unicode

Total characters64749
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiscount Store
2nd rowOrganic Store
3rd rowDiscount Store
4th rowSupermarket
5th rowDiscount Store

Common Values

ValueCountFrequency (%)
Supermarket 1998
40.0%
Discount Store 1259
25.2%
Organic Store 772
 
15.4%
Convenience Store 505
 
10.1%
Warehouse Club 466
 
9.3%

Length

2025-08-12T08:52:55.624030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T08:52:55.713491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
store 2536
31.7%
supermarket 1998
25.0%
discount 1259
15.7%
organic 772
 
9.6%
convenience 505
 
6.3%
warehouse 466
 
5.8%
club 466
 
5.8%

Most occurring characters

ValueCountFrequency (%)
e 8979
13.9%
r 7770
12.0%
t 5793
 
8.9%
o 4766
 
7.4%
S 4534
 
7.0%
u 4189
 
6.5%
n 3546
 
5.5%
a 3236
 
5.0%
3002
 
4.6%
i 2536
 
3.9%
Other values (14) 16398
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53745
83.0%
Uppercase Letter 8002
 
12.4%
Space Separator 3002
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8979
16.7%
r 7770
14.5%
t 5793
10.8%
o 4766
8.9%
u 4189
7.8%
n 3546
 
6.6%
a 3236
 
6.0%
i 2536
 
4.7%
c 2536
 
4.7%
k 1998
 
3.7%
Other values (8) 8396
15.6%
Uppercase Letter
ValueCountFrequency (%)
S 4534
56.7%
D 1259
 
15.7%
C 971
 
12.1%
O 772
 
9.6%
W 466
 
5.8%
Space Separator
ValueCountFrequency (%)
3002
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61747
95.4%
Common 3002
 
4.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8979
14.5%
r 7770
12.6%
t 5793
9.4%
o 4766
 
7.7%
S 4534
 
7.3%
u 4189
 
6.8%
n 3546
 
5.7%
a 3236
 
5.2%
i 2536
 
4.1%
c 2536
 
4.1%
Other values (13) 13862
22.4%
Common
ValueCountFrequency (%)
3002
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8979
13.9%
r 7770
12.0%
t 5793
 
8.9%
o 4766
 
7.4%
S 4534
 
7.0%
u 4189
 
6.5%
n 3546
 
5.5%
a 3236
 
5.0%
3002
 
4.6%
i 2536
 
3.9%
Other values (14) 16398
25.3%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size326.1 KiB
Credit Card
2236 
Debit Card
1552 
Cash
734 
Mobile Payment
373 
Check
 
105

Length

Max length14
Median length11
Mean length9.7598
Min length4

Characters and Unicode

Total characters48799
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCredit Card
3rd rowMobile Payment
4th rowCredit Card
5th rowMobile Payment

Common Values

ValueCountFrequency (%)
Credit Card 2236
44.7%
Debit Card 1552
31.0%
Cash 734
 
14.7%
Mobile Payment 373
 
7.5%
Check 105
 
2.1%

Length

2025-08-12T08:52:55.915258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T08:52:56.003989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
card 3788
41.3%
credit 2236
24.4%
debit 1552
16.9%
cash 734
 
8.0%
mobile 373
 
4.1%
payment 373
 
4.1%
check 105
 
1.1%

Most occurring characters

ValueCountFrequency (%)
C 6863
14.1%
d 6024
12.3%
r 6024
12.3%
a 4895
10.0%
e 4639
9.5%
i 4161
8.5%
t 4161
8.5%
4161
8.5%
b 1925
 
3.9%
D 1552
 
3.2%
Other values (11) 4394
9.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35477
72.7%
Uppercase Letter 9161
 
18.8%
Space Separator 4161
 
8.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 6024
17.0%
r 6024
17.0%
a 4895
13.8%
e 4639
13.1%
i 4161
11.7%
t 4161
11.7%
b 1925
 
5.4%
h 839
 
2.4%
s 734
 
2.1%
o 373
 
1.1%
Other values (6) 1702
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
C 6863
74.9%
D 1552
 
16.9%
M 373
 
4.1%
P 373
 
4.1%
Space Separator
ValueCountFrequency (%)
4161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44638
91.5%
Common 4161
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 6863
15.4%
d 6024
13.5%
r 6024
13.5%
a 4895
11.0%
e 4639
10.4%
i 4161
9.3%
t 4161
9.3%
b 1925
 
4.3%
D 1552
 
3.5%
h 839
 
1.9%
Other values (10) 3555
8.0%
Common
ValueCountFrequency (%)
4161
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 6863
14.1%
d 6024
12.3%
r 6024
12.3%
a 4895
10.0%
e 4639
9.5%
i 4161
8.5%
t 4161
8.5%
4161
8.5%
b 1925
 
3.9%
D 1552
 
3.2%
Other values (11) 4394
9.0%

produce_spending_pct
Real number (ℝ)

Distinct590
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.65578
Minimum0.2
Maximum72.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:56.099667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile9.2
Q120.6
median29.4
Q338.5
95-th percentile50.4
Maximum72.8
Range72.6
Interquartile range (IQR)17.9

Descriptive statistics

Standard deviation12.38115
Coefficient of variation (CV)0.41749533
Kurtosis-0.46495369
Mean29.65578
Median Absolute Deviation (MAD)8.95
Skewness0.072345641
Sum148278.9
Variance153.29287
MonotonicityNot monotonic
2025-08-12T08:52:56.203278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.9 25
 
0.5%
29 25
 
0.5%
28.3 25
 
0.5%
26.4 23
 
0.5%
30.6 22
 
0.4%
29.1 21
 
0.4%
28.5 21
 
0.4%
37 21
 
0.4%
38.1 21
 
0.4%
33 21
 
0.4%
Other values (580) 4775
95.5%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
1.1 1
 
< 0.1%
1.3 4
0.1%
1.5 3
0.1%
1.6 1
 
< 0.1%
1.7 1
 
< 0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.2 1
 
< 0.1%
ValueCountFrequency (%)
72.8 1
< 0.1%
69.9 1
< 0.1%
69.8 1
< 0.1%
69.4 1
< 0.1%
67.1 1
< 0.1%
66.1 1
< 0.1%
65.4 1
< 0.1%
65 1
< 0.1%
63.9 1
< 0.1%
63.6 1
< 0.1%

dairy_spending_pct
Real number (ℝ)

Distinct442
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.16732
Minimum0.3
Maximum56.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:56.318420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile4.3
Q110.4
median16.3
Q322.8
95-th percentile33.2
Maximum56.2
Range55.9
Interquartile range (IQR)12.4

Descriptive statistics

Standard deviation8.9216935
Coefficient of variation (CV)0.51969052
Kurtosis0.27011063
Mean17.16732
Median Absolute Deviation (MAD)6.2
Skewness0.60051464
Sum85836.6
Variance79.596615
MonotonicityNot monotonic
2025-08-12T08:52:56.418083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.7 31
 
0.6%
18.3 30
 
0.6%
17.1 30
 
0.6%
12.9 30
 
0.6%
12.8 29
 
0.6%
8.7 29
 
0.6%
19.2 29
 
0.6%
21.7 28
 
0.6%
13.2 28
 
0.6%
18 27
 
0.5%
Other values (432) 4709
94.2%
ValueCountFrequency (%)
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 3
0.1%
0.9 5
0.1%
1.1 4
0.1%
1.2 4
0.1%
1.3 7
0.1%
1.4 6
0.1%
1.5 5
0.1%
1.6 3
0.1%
ValueCountFrequency (%)
56.2 1
< 0.1%
54.7 1
< 0.1%
53.8 1
< 0.1%
52.5 1
< 0.1%
52.3 1
< 0.1%
51.5 1
< 0.1%
50.8 1
< 0.1%
50.7 1
< 0.1%
50.2 1
< 0.1%
49.5 1
< 0.1%

meat_spending_pct
Real number (ℝ)

Distinct539
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.0018
Minimum0.4
Maximum74.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:56.525521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile7.2
Q116.7
median24.7
Q332.5
95-th percentile44
Maximum74.7
Range74.3
Interquartile range (IQR)15.8

Descriptive statistics

Standard deviation11.186974
Coefficient of variation (CV)0.44744676
Kurtosis-0.21051974
Mean25.0018
Median Absolute Deviation (MAD)7.9
Skewness0.27123821
Sum125009
Variance125.14839
MonotonicityNot monotonic
2025-08-12T08:52:56.629200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.7 25
 
0.5%
19.6 24
 
0.5%
21.8 23
 
0.5%
27.2 23
 
0.5%
32.5 23
 
0.5%
26.2 23
 
0.5%
22.8 22
 
0.4%
20.1 22
 
0.4%
18.6 22
 
0.4%
19.1 22
 
0.4%
Other values (529) 4771
95.4%
ValueCountFrequency (%)
0.4 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
1 1
 
< 0.1%
1.1 1
 
< 0.1%
1.2 2
< 0.1%
1.5 2
< 0.1%
1.7 3
0.1%
1.8 2
< 0.1%
1.9 3
0.1%
ValueCountFrequency (%)
74.7 1
< 0.1%
68.2 1
< 0.1%
64.9 1
< 0.1%
63.8 1
< 0.1%
62.5 1
< 0.1%
61.7 1
< 0.1%
60.6 1
< 0.1%
60.2 1
< 0.1%
60 1
< 0.1%
59.8 1
< 0.1%

bakery_spending_pct
Real number (ℝ)

Distinct326
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.13552
Minimum0.1
Maximum63.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:56.740243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile2.4
Q16.3
median10.3
Q314.9
95-th percentile22.8
Maximum63.1
Range63
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation6.3766492
Coefficient of variation (CV)0.57264045
Kurtosis1.4837308
Mean11.13552
Median Absolute Deviation (MAD)4.3
Skewness0.87735402
Sum55677.6
Variance40.661655
MonotonicityNot monotonic
2025-08-12T08:52:56.841861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.8 48
 
1.0%
11.5 42
 
0.8%
9.9 42
 
0.8%
9.4 42
 
0.8%
7.8 41
 
0.8%
8.4 40
 
0.8%
7.1 39
 
0.8%
12.1 39
 
0.8%
6.2 37
 
0.7%
9.5 37
 
0.7%
Other values (316) 4593
91.9%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.2 4
 
0.1%
0.3 3
 
0.1%
0.5 2
 
< 0.1%
0.6 5
 
0.1%
0.8 12
0.2%
0.9 13
0.3%
1 13
0.3%
1.1 12
0.2%
1.2 11
0.2%
ValueCountFrequency (%)
63.1 1
 
< 0.1%
39.1 1
 
< 0.1%
38.7 1
 
< 0.1%
38.4 1
 
< 0.1%
37.7 1
 
< 0.1%
37.5 1
 
< 0.1%
37.3 3
0.1%
37.1 1
 
< 0.1%
36.5 1
 
< 0.1%
36.1 1
 
< 0.1%

frozen_spending_pct
Real number (ℝ)

Distinct433
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.04026
Minimum0.6
Maximum60.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:56.949200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile4.5
Q110.5
median16.1
Q322.5
95-th percentile32.905
Maximum60.1
Range59.5
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.7953415
Coefficient of variation (CV)0.51615067
Kurtosis0.51306372
Mean17.04026
Median Absolute Deviation (MAD)6
Skewness0.66523382
Sum85201.3
Variance77.358033
MonotonicityNot monotonic
2025-08-12T08:52:57.051670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.3 32
 
0.6%
17.6 32
 
0.6%
11.1 31
 
0.6%
11.8 31
 
0.6%
13.4 31
 
0.6%
13.3 30
 
0.6%
10.8 30
 
0.6%
12.7 30
 
0.6%
12 30
 
0.6%
15.7 30
 
0.6%
Other values (423) 4693
93.9%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 2
 
< 0.1%
0.8 3
0.1%
1 4
0.1%
1.1 1
 
< 0.1%
1.2 2
 
< 0.1%
1.3 6
0.1%
1.4 7
0.1%
1.5 7
0.1%
1.6 5
0.1%
ValueCountFrequency (%)
60.1 1
< 0.1%
57.3 1
< 0.1%
56.4 1
< 0.1%
55.7 1
< 0.1%
54.1 1
< 0.1%
51.8 1
< 0.1%
51.6 1
< 0.1%
50.7 1
< 0.1%
50.3 2
< 0.1%
49.6 1
< 0.1%
Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size313.8 KiB
Saturday
1300 
Friday
1027 
Thursday
690 
Wednesday
574 
Tuesday
527 
Other values (2)
882 

Length

Max length9
Median length8
Mean length7.2458
Min length6

Characters and Unicode

Total characters36229
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowSaturday
3rd rowFriday
4th rowSunday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Saturday 1300
26.0%
Friday 1027
20.5%
Thursday 690
13.8%
Wednesday 574
11.5%
Tuesday 527
10.5%
Sunday 510
 
10.2%
Monday 372
 
7.4%

Length

2025-08-12T08:52:57.149485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T08:52:57.243845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
saturday 1300
26.0%
friday 1027
20.5%
thursday 690
13.8%
wednesday 574
11.5%
tuesday 527
10.5%
sunday 510
 
10.2%
monday 372
 
7.4%

Most occurring characters

ValueCountFrequency (%)
a 6300
17.4%
d 5574
15.4%
y 5000
13.8%
u 3027
8.4%
r 3017
8.3%
S 1810
 
5.0%
s 1791
 
4.9%
e 1675
 
4.6%
n 1456
 
4.0%
t 1300
 
3.6%
Other values (7) 5279
14.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31229
86.2%
Uppercase Letter 5000
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6300
20.2%
d 5574
17.8%
y 5000
16.0%
u 3027
9.7%
r 3017
9.7%
s 1791
 
5.7%
e 1675
 
5.4%
n 1456
 
4.7%
t 1300
 
4.2%
i 1027
 
3.3%
Other values (2) 1062
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
S 1810
36.2%
T 1217
24.3%
F 1027
20.5%
W 574
 
11.5%
M 372
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 36229
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6300
17.4%
d 5574
15.4%
y 5000
13.8%
u 3027
8.4%
r 3017
8.3%
S 1810
 
5.0%
s 1791
 
4.9%
e 1675
 
4.6%
n 1456
 
4.0%
t 1300
 
3.6%
Other values (7) 5279
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6300
17.4%
d 5574
15.4%
y 5000
13.8%
u 3027
8.4%
r 3017
8.3%
S 1810
 
5.0%
s 1791
 
4.9%
e 1675
 
4.6%
n 1456
 
4.0%
t 1300
 
3.6%
Other values (7) 5279
14.6%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size317.5 KiB
Afternoon
2520 
Morning
1265 
Evening
1215 

Length

Max length9
Median length9
Mean length8.008
Min length7

Characters and Unicode

Total characters40040
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowEvening
3rd rowAfternoon
4th rowMorning
5th rowEvening

Common Values

ValueCountFrequency (%)
Afternoon 2520
50.4%
Morning 1265
25.3%
Evening 1215
24.3%

Length

2025-08-12T08:52:57.342491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T08:52:57.429152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
afternoon 2520
50.4%
morning 1265
25.3%
evening 1215
24.3%

Most occurring characters

ValueCountFrequency (%)
n 10000
25.0%
o 6305
15.7%
r 3785
 
9.5%
e 3735
 
9.3%
A 2520
 
6.3%
f 2520
 
6.3%
t 2520
 
6.3%
i 2480
 
6.2%
g 2480
 
6.2%
M 1265
 
3.2%
Other values (2) 2430
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35040
87.5%
Uppercase Letter 5000
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10000
28.5%
o 6305
18.0%
r 3785
 
10.8%
e 3735
 
10.7%
f 2520
 
7.2%
t 2520
 
7.2%
i 2480
 
7.1%
g 2480
 
7.1%
v 1215
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
A 2520
50.4%
M 1265
25.3%
E 1215
24.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 40040
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10000
25.0%
o 6305
15.7%
r 3785
 
9.5%
e 3735
 
9.3%
A 2520
 
6.3%
f 2520
 
6.3%
t 2520
 
6.3%
i 2480
 
6.2%
g 2480
 
6.2%
M 1265
 
3.2%
Other values (2) 2430
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10000
25.0%
o 6305
15.7%
r 3785
 
9.5%
e 3735
 
9.3%
A 2520
 
6.3%
f 2520
 
6.3%
t 2520
 
6.3%
i 2480
 
6.2%
g 2480
 
6.2%
M 1265
 
3.2%
Other values (2) 2430
 
6.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
3259 
False
1741 
ValueCountFrequency (%)
True 3259
65.2%
False 1741
34.8%
2025-08-12T08:52:57.503704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

years_as_customer
Real number (ℝ)

Distinct143
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.93446
Minimum0.1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:57.708036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.9
median2.1
Q34.2
95-th percentile8.7
Maximum15
Range14.9
Interquartile range (IQR)3.3

Descriptive statistics

Standard deviation2.8134188
Coefficient of variation (CV)0.95875181
Kurtosis2.8180447
Mean2.93446
Median Absolute Deviation (MAD)1.5
Skewness1.5988251
Sum14672.3
Variance7.9153256
MonotonicityNot monotonic
2025-08-12T08:52:57.812290image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 269
 
5.4%
0.2 183
 
3.7%
0.3 164
 
3.3%
0.4 147
 
2.9%
0.5 131
 
2.6%
1 131
 
2.6%
0.6 129
 
2.6%
1.3 121
 
2.4%
1.6 115
 
2.3%
0.9 113
 
2.3%
Other values (133) 3497
69.9%
ValueCountFrequency (%)
0.1 269
5.4%
0.2 183
3.7%
0.3 164
3.3%
0.4 147
2.9%
0.5 131
2.6%
0.6 129
2.6%
0.7 107
 
2.1%
0.8 106
 
2.1%
0.9 113
2.3%
1 131
2.6%
ValueCountFrequency (%)
15 23
0.5%
14.9 2
 
< 0.1%
14.7 2
 
< 0.1%
14.6 3
 
0.1%
14.3 3
 
0.1%
14.1 1
 
< 0.1%
13.9 2
 
< 0.1%
13.8 1
 
< 0.1%
13.7 1
 
< 0.1%
13.6 1
 
< 0.1%

customer_satisfaction
Real number (ℝ)

MISSING 

Distinct64
Distinct (%)1.4%
Missing391
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean7.5729008
Minimum3.7
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:57.923350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.7
5-th percentile5.8
Q16.9
median7.6
Q38.3
95-th percentile9.4
Maximum10
Range6.3
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.0736593
Coefficient of variation (CV)0.14177648
Kurtosis-0.10034999
Mean7.5729008
Median Absolute Deviation (MAD)0.7
Skewness-0.094162762
Sum34903.5
Variance1.1527442
MonotonicityNot monotonic
2025-08-12T08:52:58.027961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.6 186
 
3.7%
7.1 177
 
3.5%
7.9 174
 
3.5%
8 171
 
3.4%
7.7 168
 
3.4%
7.8 167
 
3.3%
7.5 164
 
3.3%
7.2 161
 
3.2%
6.9 155
 
3.1%
7 154
 
3.1%
Other values (54) 2932
58.6%
(Missing) 391
 
7.8%
ValueCountFrequency (%)
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4 1
 
< 0.1%
4.1 1
 
< 0.1%
4.2 3
0.1%
4.3 5
0.1%
4.4 1
 
< 0.1%
4.5 5
0.1%
4.6 3
0.1%
ValueCountFrequency (%)
10 64
1.3%
9.9 15
 
0.3%
9.8 22
 
0.4%
9.7 26
0.5%
9.6 28
0.6%
9.5 44
0.9%
9.4 52
1.0%
9.3 45
0.9%
9.2 52
1.0%
9.1 58
1.2%

organic_preference_pct
Real number (ℝ)

MISSING  ZEROS 

Distinct657
Distinct (%)13.8%
Missing252
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean27.083446
Minimum0
Maximum80
Zeros238
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:58.141448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.035
Q116
median26.7
Q337.6
95-th percentile53.3
Maximum80
Range80
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation15.520421
Coefficient of variation (CV)0.57305934
Kurtosis-0.39381375
Mean27.083446
Median Absolute Deviation (MAD)10.8
Skewness0.23048586
Sum128592.2
Variance240.88348
MonotonicityNot monotonic
2025-08-12T08:52:58.248962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 238
 
4.8%
14.9 22
 
0.4%
30.9 21
 
0.4%
25.7 20
 
0.4%
33.9 19
 
0.4%
19.2 17
 
0.3%
26.5 17
 
0.3%
33.6 16
 
0.3%
20.1 16
 
0.3%
32.7 16
 
0.3%
Other values (647) 4346
86.9%
(Missing) 252
 
5.0%
ValueCountFrequency (%)
0 238
4.8%
0.1 3
 
0.1%
0.2 4
 
0.1%
0.3 3
 
0.1%
0.4 2
 
< 0.1%
0.5 1
 
< 0.1%
0.6 3
 
0.1%
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
0.9 4
 
0.1%
ValueCountFrequency (%)
80 1
< 0.1%
78.8 1
< 0.1%
75.6 1
< 0.1%
74.1 1
< 0.1%
73.4 1
< 0.1%
72.3 1
< 0.1%
71.6 1
< 0.1%
71.5 1
< 0.1%
71 1
< 0.1%
70.9 1
< 0.1%

coupon_usage_pct
Real number (ℝ)

Distinct450
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.1025
Minimum0.3
Maximum48.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:58.361190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile4.9
Q112
median19.4
Q327.7
95-th percentile37.7
Maximum48.4
Range48.1
Interquartile range (IQR)15.7

Descriptive statistics

Standard deviation10.152463
Coefficient of variation (CV)0.50503487
Kurtosis-0.71274571
Mean20.1025
Median Absolute Deviation (MAD)7.7
Skewness0.27280359
Sum100512.5
Variance103.07251
MonotonicityNot monotonic
2025-08-12T08:52:58.468006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.3 28
 
0.6%
12 27
 
0.5%
14.3 25
 
0.5%
19.5 25
 
0.5%
15.6 25
 
0.5%
21.6 24
 
0.5%
19.7 24
 
0.5%
17.1 24
 
0.5%
17.4 24
 
0.5%
14.2 23
 
0.5%
Other values (440) 4751
95.0%
ValueCountFrequency (%)
0.3 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 3
0.1%
0.7 1
 
< 0.1%
0.8 2
< 0.1%
0.9 2
< 0.1%
1 2
< 0.1%
1.1 4
0.1%
1.2 1
 
< 0.1%
ValueCountFrequency (%)
48.4 1
 
< 0.1%
47.3 1
 
< 0.1%
47.1 1
 
< 0.1%
47 2
< 0.1%
46.6 2
< 0.1%
46.4 1
 
< 0.1%
46.2 1
 
< 0.1%
46 1
 
< 0.1%
45.9 3
0.1%
45.7 3
0.1%

online_shopping_pct
Real number (ℝ)

Distinct481
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.38406
Minimum0
Maximum55.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-08-12T08:52:58.572517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.4
Q17.9
median14.7
Q323
95-th percentile36.7
Maximum55.5
Range55.5
Interquartile range (IQR)15.1

Descriptive statistics

Standard deviation10.55062
Coefficient of variation (CV)0.64395636
Kurtosis0.038447939
Mean16.38406
Median Absolute Deviation (MAD)7.3
Skewness0.72463781
Sum81920.3
Variance111.31558
MonotonicityNot monotonic
2025-08-12T08:52:58.670830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 29
 
0.6%
7.6 28
 
0.6%
9 28
 
0.6%
9.4 28
 
0.6%
8.4 27
 
0.5%
7.3 27
 
0.5%
13.3 26
 
0.5%
9.6 26
 
0.5%
4.5 25
 
0.5%
17.5 25
 
0.5%
Other values (471) 4731
94.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.1 4
 
0.1%
0.2 6
 
0.1%
0.3 3
 
0.1%
0.4 8
0.2%
0.5 8
0.2%
0.6 17
0.3%
0.7 7
0.1%
0.8 11
0.2%
0.9 6
 
0.1%
ValueCountFrequency (%)
55.5 1
< 0.1%
54.6 1
< 0.1%
52.6 1
< 0.1%
52.3 1
< 0.1%
52.1 1
< 0.1%
52 1
< 0.1%
51.9 1
< 0.1%
51.6 2
< 0.1%
51.4 1
< 0.1%
51.3 1
< 0.1%

Interactions

2025-08-12T08:52:50.401989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:23.703257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:25.491785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:27.081245image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:28.833623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:30.515672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:32.165554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:33.808941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:35.540154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:37.282840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:38.829373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:40.549998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:42.237508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:43.807434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:45.501706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:47.168320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:48.889156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:50.490002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:23.881674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:25.584309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:27.178296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:28.922443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:30.612955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:32.251997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:33.901114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:35.631353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:37.371069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:38.921577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:40.639737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:42.326379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:43.898329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:45.590702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:47.261542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:48.977046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:50.580122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:23.973389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2025-08-12T08:52:50.045573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:51.793143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:25.078482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:26.802268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:28.550158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:30.246453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:31.876370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:33.543287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:35.263335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:36.856908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:38.559196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:40.274152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:41.964454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:43.534604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:45.227773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:46.897144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:48.490249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:50.135120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:51.887278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:25.174815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:26.899126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:28.650261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:30.341419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:31.979225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:33.638049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:35.360759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:36.957321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:38.654833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:40.371691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:42.061519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:43.631268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:45.323872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:46.991805image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:48.588801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:50.229252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:51.973375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:25.262766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:26.990779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:28.742065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:30.429132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:32.072628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:33.723118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:35.450409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:37.050699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:38.741549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:40.461086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:42.149085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:43.719300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:45.412857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:47.079930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:48.679779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2025-08-12T08:52:50.315658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2025-08-12T08:52:58.782118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ageannual_incomecredit_scorehousehold_sizeshopping_frequency_monthlyavg_basket_sizemonthly_spendingproduce_spending_pctdairy_spending_pctmeat_spending_pctbakery_spending_pctfrozen_spending_pctyears_as_customercustomer_satisfactionorganic_preference_pctcoupon_usage_pctonline_shopping_pctgendercityprovincepreferred_store_typepreferred_payment_methodpreferred_shopping_daypreferred_shopping_timeloyalty_member
age1.0000.5060.622-0.027-0.0040.2050.131-0.0050.0030.014-0.010-0.0150.0130.157-0.072-0.006-0.0130.0000.0000.0000.0000.0070.0180.0000.000
annual_income0.5061.0000.647-0.006-0.0180.3950.2410.000-0.0070.015-0.032-0.0030.0090.3050.245-0.009-0.0160.0000.0000.0000.0000.0000.0000.0000.025
credit_score0.6220.6471.000-0.0080.0020.2600.170-0.015-0.0170.0150.013-0.011-0.0010.2130.082-0.003-0.0120.0000.0000.0000.0050.0080.0030.0000.000
household_size-0.027-0.006-0.0081.000-0.0150.006-0.003-0.0100.0200.006-0.007-0.0130.0120.012-0.008-0.000-0.0060.0070.0000.0000.0300.0080.0060.0170.031
shopping_frequency_monthly-0.004-0.0180.002-0.0151.000-0.0200.7140.034-0.0380.017-0.007-0.023-0.023-0.023-0.014-0.004-0.0220.0000.0110.0000.0000.0000.0000.0000.000
avg_basket_size0.2050.3950.2600.006-0.0201.0000.5930.016-0.013-0.007-0.015-0.0060.0170.1380.112-0.013-0.0060.0440.0000.0000.0000.0000.0280.0000.000
monthly_spending0.1310.2410.170-0.0030.7140.5931.0000.037-0.0330.004-0.015-0.0220.0080.0720.062-0.006-0.0200.0350.0000.0000.0070.0120.0000.0300.000
produce_spending_pct-0.0050.000-0.015-0.0100.0340.0160.0371.000-0.335-0.434-0.219-0.2890.0120.0080.0030.0040.0170.0140.0090.0170.0180.0000.0190.0210.000
dairy_spending_pct0.003-0.007-0.0170.020-0.038-0.013-0.033-0.3351.000-0.218-0.082-0.1690.015-0.0160.014-0.0120.0080.0190.0120.0250.0250.0220.0090.0070.028
meat_spending_pct0.0140.0150.0150.0060.017-0.0070.004-0.434-0.2181.000-0.182-0.253-0.034-0.001-0.0160.001-0.0210.0160.0180.0120.0000.0210.0000.0000.006
bakery_spending_pct-0.010-0.0320.013-0.007-0.007-0.015-0.015-0.219-0.082-0.1821.000-0.075-0.001-0.004-0.0190.007-0.0120.0520.0080.0120.0000.0000.0100.0220.000
frozen_spending_pct-0.015-0.003-0.011-0.013-0.023-0.006-0.022-0.289-0.169-0.253-0.0751.0000.009-0.0020.0080.0040.0070.0080.0100.0200.0000.0000.0000.0000.000
years_as_customer0.0130.009-0.0010.012-0.0230.0170.0080.0120.015-0.034-0.0010.0091.0000.033-0.0030.0180.0340.0000.0130.0160.0070.0180.0160.0000.035
customer_satisfaction0.1570.3050.2130.012-0.0230.1380.0720.008-0.016-0.001-0.004-0.0020.0331.0000.0680.014-0.0310.0000.0000.0180.0040.0120.0000.0060.227
organic_preference_pct-0.0720.2450.082-0.008-0.0140.1120.0620.0030.014-0.016-0.0190.008-0.0030.0681.0000.009-0.0190.0120.0000.0000.0140.0000.0000.0000.009
coupon_usage_pct-0.006-0.009-0.003-0.000-0.004-0.013-0.0060.004-0.0120.0010.0070.0040.0180.0140.0091.0000.0140.0000.0130.0000.0300.0110.0000.0000.016
online_shopping_pct-0.013-0.016-0.012-0.006-0.022-0.006-0.0200.0170.008-0.021-0.0120.0070.034-0.031-0.0190.0141.0000.0000.0190.0210.0110.0000.0090.0000.000
gender0.0000.0000.0000.0070.0000.0440.0350.0140.0190.0160.0520.0080.0000.0000.0120.0000.0001.0000.0000.0100.0100.0000.0160.0160.009
city0.0000.0000.0000.0000.0110.0000.0000.0090.0120.0180.0080.0100.0130.0000.0000.0130.0190.0001.0000.9990.0050.0180.0180.0000.016
province0.0000.0000.0000.0000.0000.0000.0000.0170.0250.0120.0120.0200.0160.0180.0000.0000.0210.0100.9991.0000.0000.0140.0000.0000.000
preferred_store_type0.0000.0000.0050.0300.0000.0000.0070.0180.0250.0000.0000.0000.0070.0040.0140.0300.0110.0100.0050.0001.0000.0000.0080.0100.000
preferred_payment_method0.0070.0000.0080.0080.0000.0000.0120.0000.0220.0210.0000.0000.0180.0120.0000.0110.0000.0000.0180.0140.0001.0000.0240.0000.010
preferred_shopping_day0.0180.0000.0030.0060.0000.0280.0000.0190.0090.0000.0100.0000.0160.0000.0000.0000.0090.0160.0180.0000.0080.0241.0000.0070.000
preferred_shopping_time0.0000.0000.0000.0170.0000.0000.0300.0210.0070.0000.0220.0000.0000.0060.0000.0000.0000.0160.0000.0000.0100.0000.0071.0000.000
loyalty_member0.0000.0250.0000.0310.0000.0000.0000.0000.0280.0060.0000.0000.0350.2270.0090.0160.0000.0090.0160.0000.0000.0100.0000.0001.000
2025-08-12T08:52:59.011001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ageannual_incomecredit_scorehousehold_sizeshopping_frequency_monthlyavg_basket_sizemonthly_spendingproduce_spending_pctdairy_spending_pctmeat_spending_pctbakery_spending_pctfrozen_spending_pctloyalty_memberyears_as_customercustomer_satisfactionorganic_preference_pctcoupon_usage_pctonline_shopping_pct
age1.0000.5160.627-0.024-0.0060.2040.132-0.0070.0050.012-0.008-0.005-0.0130.0080.157-0.068-0.003-0.005
annual_income0.5161.0000.657-0.008-0.0230.4050.245-0.001-0.0050.012-0.0210.006-0.0010.0040.3200.260-0.015-0.011
credit_score0.6270.6571.000-0.0120.0030.2640.179-0.012-0.0130.0160.0110.0020.005-0.0020.2200.092-0.002-0.009
household_size-0.024-0.008-0.0121.000-0.0160.008-0.003-0.0080.0210.001-0.003-0.009-0.0140.0180.014-0.0070.002-0.007
shopping_frequency_monthly-0.006-0.0230.003-0.0161.000-0.0210.7160.035-0.0400.019-0.013-0.024-0.022-0.016-0.019-0.012-0.003-0.016
avg_basket_size0.2040.4050.2640.008-0.0211.0000.5950.015-0.007-0.006-0.006-0.001-0.0160.0100.1380.123-0.015-0.005
monthly_spending0.1320.2450.179-0.0030.7160.5951.0000.036-0.0340.007-0.011-0.018-0.0260.0100.0750.064-0.004-0.011
produce_spending_pct-0.007-0.001-0.012-0.0080.0350.0150.0361.000-0.349-0.452-0.237-0.307-0.0000.0070.0110.0080.0050.017
dairy_spending_pct0.005-0.005-0.0130.021-0.040-0.007-0.034-0.3491.000-0.233-0.075-0.173-0.0210.011-0.0120.013-0.0110.000
meat_spending_pct0.0120.0120.0160.0010.019-0.0060.007-0.452-0.2331.000-0.187-0.2640.001-0.030-0.000-0.0180.001-0.021
bakery_spending_pct-0.008-0.0210.011-0.003-0.013-0.006-0.011-0.237-0.075-0.1871.000-0.0770.0160.007-0.004-0.0150.003-0.009
frozen_spending_pct-0.0050.0060.002-0.009-0.024-0.001-0.018-0.307-0.173-0.264-0.0771.0000.0090.011-0.0000.0090.0000.009
loyalty_member-0.013-0.0010.005-0.014-0.022-0.016-0.026-0.000-0.0210.0010.0160.0091.0000.0240.235-0.0170.0100.021
years_as_customer0.0080.004-0.0020.018-0.0160.0100.0100.0070.011-0.0300.0070.0110.0241.0000.030-0.0080.0120.031
customer_satisfaction0.1570.3200.2200.014-0.0190.1380.0750.011-0.012-0.000-0.004-0.0000.2350.0301.0000.0720.013-0.028
organic_preference_pct-0.0680.2600.092-0.007-0.0120.1230.0640.0080.013-0.018-0.0150.009-0.017-0.0080.0721.0000.007-0.020
coupon_usage_pct-0.003-0.015-0.0020.002-0.003-0.015-0.0040.005-0.0110.0010.0030.0000.0100.0120.0130.0071.0000.014
online_shopping_pct-0.005-0.011-0.009-0.007-0.016-0.005-0.0110.0170.000-0.021-0.0090.0090.0210.031-0.028-0.0200.0141.000
2025-08-12T08:52:59.204893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ageannual_incomecredit_scorehousehold_sizeshopping_frequency_monthlyavg_basket_sizemonthly_spendingproduce_spending_pctdairy_spending_pctmeat_spending_pctbakery_spending_pctfrozen_spending_pctloyalty_memberyears_as_customercustomer_satisfactionorganic_preference_pctcoupon_usage_pctonline_shopping_pct
age1.0000.5060.622-0.027-0.0040.2050.131-0.0050.0030.014-0.010-0.015-0.0130.0130.157-0.072-0.006-0.013
annual_income0.5061.0000.647-0.006-0.0180.3950.2410.000-0.0070.015-0.032-0.003-0.0010.0090.3050.245-0.009-0.016
credit_score0.6220.6471.000-0.0080.0020.2600.170-0.015-0.0170.0150.013-0.0110.002-0.0010.2130.082-0.003-0.012
household_size-0.027-0.006-0.0081.000-0.0150.006-0.003-0.0100.0200.006-0.007-0.013-0.0100.0120.012-0.008-0.000-0.006
shopping_frequency_monthly-0.004-0.0180.002-0.0151.000-0.0200.7140.034-0.0380.017-0.007-0.023-0.018-0.023-0.023-0.014-0.004-0.022
avg_basket_size0.2050.3950.2600.006-0.0201.0000.5930.016-0.013-0.007-0.015-0.006-0.0160.0170.1380.112-0.013-0.006
monthly_spending0.1310.2410.170-0.0030.7140.5931.0000.037-0.0330.004-0.015-0.022-0.0260.0080.0720.062-0.006-0.020
produce_spending_pct-0.0050.000-0.015-0.0100.0340.0160.0371.000-0.335-0.434-0.219-0.2890.0000.0120.0080.0030.0040.017
dairy_spending_pct0.003-0.007-0.0170.020-0.038-0.013-0.033-0.3351.000-0.218-0.082-0.169-0.0250.015-0.0160.014-0.0120.008
meat_spending_pct0.0140.0150.0150.0060.017-0.0070.004-0.434-0.2181.000-0.182-0.253-0.003-0.034-0.001-0.0160.001-0.021
bakery_spending_pct-0.010-0.0320.013-0.007-0.007-0.015-0.015-0.219-0.082-0.1821.000-0.0750.014-0.001-0.004-0.0190.007-0.012
frozen_spending_pct-0.015-0.003-0.011-0.013-0.023-0.006-0.022-0.289-0.169-0.253-0.0751.0000.0030.009-0.0020.0080.0040.007
loyalty_member-0.013-0.0010.002-0.010-0.018-0.016-0.0260.000-0.025-0.0030.0140.0031.0000.0330.229-0.0190.0090.021
years_as_customer0.0130.009-0.0010.012-0.0230.0170.0080.0120.015-0.034-0.0010.0090.0331.0000.033-0.0030.0180.034
customer_satisfaction0.1570.3050.2130.012-0.0230.1380.0720.008-0.016-0.001-0.004-0.0020.2290.0331.0000.0680.014-0.031
organic_preference_pct-0.0720.2450.082-0.008-0.0140.1120.0620.0030.014-0.016-0.0190.008-0.019-0.0030.0681.0000.009-0.019
coupon_usage_pct-0.006-0.009-0.003-0.000-0.004-0.013-0.0060.004-0.0120.0010.0070.0040.0090.0180.0140.0091.0000.014
online_shopping_pct-0.013-0.016-0.012-0.006-0.022-0.006-0.0200.0170.008-0.021-0.0120.0070.0210.034-0.031-0.0190.0141.000
2025-08-12T08:52:59.523579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
agegenderannual_incomecredit_scorecityprovincehousehold_sizeshopping_frequency_monthlyavg_basket_sizemonthly_spendingpreferred_store_typepreferred_payment_methodproduce_spending_pctdairy_spending_pctmeat_spending_pctbakery_spending_pctfrozen_spending_pctpreferred_shopping_daypreferred_shopping_timeloyalty_memberyears_as_customercustomer_satisfactionorganic_preference_pctcoupon_usage_pctonline_shopping_pct
age1.0000.0000.5020.6200.0000.0000.0380.0530.2100.1440.0000.0160.0670.0000.0830.0000.0000.0360.0000.0000.0950.1710.1750.0360.038
gender0.0001.0000.0000.0000.0000.0130.0170.0000.0750.0600.0130.0000.0240.0320.0270.0820.0200.0230.0550.0050.0000.0000.0220.0000.000
annual_income0.5020.0001.0000.6210.0000.0000.0150.0410.3950.2620.0000.0000.0000.0420.0810.0000.0690.0000.0000.0230.0620.3130.3120.0000.000
credit_score0.6200.0000.6211.0000.0000.0000.0000.1060.2640.1850.0120.0160.0300.0330.0000.0000.0260.0170.0000.0000.0000.2320.1080.0270.024
city0.0000.0000.0000.0001.0001.0000.0000.0350.0000.0000.0120.0450.0310.0400.0570.0180.0360.0360.0000.0220.0410.0000.0000.0440.059
province0.0000.0130.0000.0001.0001.0000.0000.0000.0000.0000.0000.0360.0420.0610.0290.0210.0510.0000.0000.0000.0380.0430.0000.0000.051
household_size0.0380.0170.0150.0000.0000.0001.0000.0180.0000.0000.0450.0120.0000.0340.0000.0130.0070.0110.0430.0430.0200.0000.0000.0000.008
shopping_frequency_monthly0.0530.0000.0410.1060.0350.0000.0181.0000.0000.7550.0000.0000.0440.0760.0000.0770.0000.0000.0000.0000.0290.0350.0280.0000.000
avg_basket_size0.2100.0750.3950.2640.0000.0000.0000.0001.0000.6440.0000.0000.0340.0000.0000.0000.0000.0550.0000.0000.0000.1500.1390.0000.000
monthly_spending0.1440.0600.2620.1850.0000.0000.0000.7550.6441.0000.0170.0280.0200.0000.0000.0000.0000.0000.0520.0000.0000.0460.0900.0000.000
preferred_store_type0.0000.0130.0000.0120.0120.0000.0450.0000.0000.0171.0000.0000.0420.0600.0000.0000.0000.0120.0140.0000.0170.0100.0200.0720.018
preferred_payment_method0.0160.0000.0000.0160.0450.0360.0120.0000.0000.0280.0001.0000.0000.0530.0510.0000.0000.0380.0000.0080.0430.0280.0000.0260.000
produce_spending_pct0.0670.0240.0000.0300.0310.0420.0000.0440.0340.0200.0420.0001.0000.3700.4990.1940.3140.0370.0360.0000.0440.0000.0000.0000.000
dairy_spending_pct0.0000.0320.0420.0330.0400.0610.0340.0760.0000.0000.0600.0530.3701.0000.2400.0540.1680.0170.0120.0360.0000.0000.0830.0000.000
meat_spending_pct0.0830.0270.0810.0000.0570.0290.0000.0000.0000.0000.0000.0510.4990.2401.0000.1350.2640.0000.0000.0070.0000.0000.0000.0000.000
bakery_spending_pct0.0000.0820.0000.0000.0180.0210.0130.0770.0000.0000.0000.0000.1940.0540.1351.0000.0460.0210.0350.0000.1110.0390.0000.0140.000
frozen_spending_pct0.0000.0200.0690.0260.0360.0510.0070.0000.0000.0000.0000.0000.3140.1680.2640.0461.0000.0000.0000.0000.0510.0280.0000.0760.000
preferred_shopping_day0.0360.0230.0000.0170.0360.0000.0110.0000.0550.0000.0120.0380.0370.0170.0000.0210.0001.0000.0100.0000.0320.0000.0000.0000.018
preferred_shopping_time0.0000.0550.0000.0000.0000.0000.0430.0000.0000.0520.0140.0000.0360.0120.0000.0350.0000.0101.0000.0000.0000.0100.0000.0000.000
loyalty_member0.0000.0050.0230.0000.0220.0000.0430.0000.0000.0000.0000.0080.0000.0360.0070.0000.0000.0000.0001.0000.0460.2960.0180.0210.000
years_as_customer0.0950.0000.0620.0000.0410.0380.0200.0290.0000.0000.0170.0430.0440.0000.0000.1110.0510.0320.0000.0461.0000.0000.0000.0490.067
customer_satisfaction0.1710.0000.3130.2320.0000.0430.0000.0350.1500.0460.0100.0280.0000.0000.0000.0390.0280.0000.0100.2960.0001.0000.0600.0000.041
organic_preference_pct0.1750.0220.3120.1080.0000.0000.0000.0280.1390.0900.0200.0000.0000.0830.0000.0000.0000.0000.0000.0180.0000.0601.0000.0510.000
coupon_usage_pct0.0360.0000.0000.0270.0440.0000.0000.0000.0000.0000.0720.0260.0000.0000.0000.0140.0760.0000.0000.0210.0490.0000.0511.0000.000
online_shopping_pct0.0380.0000.0000.0240.0590.0510.0080.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0180.0000.0000.0670.0410.0000.0001.000
2025-08-12T08:52:59.730752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
preferred_shopping_timegenderpreferred_payment_methodprovincecitypreferred_shopping_dayloyalty_memberpreferred_store_type
preferred_shopping_time1.0000.0160.0000.0000.0000.0070.0000.010
gender0.0161.0000.0000.0100.0000.0160.0090.010
preferred_payment_method0.0000.0001.0000.0140.0180.0240.0100.000
province0.0000.0100.0141.0000.9990.0000.0000.000
city0.0000.0000.0180.9991.0000.0180.0160.005
preferred_shopping_day0.0070.0160.0240.0000.0181.0000.0000.008
loyalty_member0.0000.0090.0100.0000.0160.0001.0000.000
preferred_store_type0.0100.0100.0000.0000.0050.0080.0001.000

Missing values

2025-08-12T08:52:52.125930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-12T08:52:52.553588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-12T08:52:52.760988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idagegenderannual_incomecredit_scorecityprovincehousehold_sizeshopping_frequency_monthlyavg_basket_sizemonthly_spendingpreferred_store_typepreferred_payment_methodproduce_spending_pctdairy_spending_pctmeat_spending_pctbakery_spending_pctfrozen_spending_pctpreferred_shopping_daypreferred_shopping_timeloyalty_memberyears_as_customercustomer_satisfactionorganic_preference_pctcoupon_usage_pctonline_shopping_pct
0CUST_00000152Male53830.66768EdmontonAB3669.88337.71Discount StoreCredit Card42.23.68.714.031.6TuesdayEveningTrue1.66.95.015.430.8
1CUST_00000242Male53671.85750HamiltonON21149.74461.44Organic StoreCredit Card27.811.342.84.813.4SaturdayEveningTrue8.18.216.212.422.5
2CUST_00000354Male68555.19677MontrealQC3856.21374.42Discount StoreMobile Payment8.418.449.313.810.1FridayAfternoonTrue0.16.446.723.031.2
3CUST_00000467Male105615.49800CalgaryAB51050.35562.05SupermarketCredit Card45.18.215.217.713.7SundayMorningTrue2.16.729.06.512.7
4CUST_00000541Male40541.22566WinnipegMB4658.16405.43Discount StoreMobile Payment35.612.228.68.614.9SaturdayEveningFalse3.16.735.810.939.5
5CUST_00000641Female56651.70703TorontoON1518.6881.23SupermarketCash23.638.618.813.25.8TuesdayMorningTrue0.19.012.428.84.6
6CUST_00000768Female104460.19850TorontoON41267.16813.85SupermarketCredit Card33.96.924.39.725.1SaturdayMorningTrue0.96.735.921.19.5
7CUST_00000856Female69802.45713TorontoON2845.57313.56Discount StoreCredit Card33.118.825.93.019.3SaturdayEveningFalse2.96.613.513.126.0
8CUST_00000937Male69808.31640Quebec CityQC6972.00720.42SupermarketDebit Card15.720.127.69.926.8SaturdayMorningTrue0.17.610.615.110.3
9CUST_00001053Male70609.16724EdmontonAB21158.35724.22SupermarketDebit Card23.03.853.14.315.8ThursdayMorningFalse7.25.817.836.634.8
customer_idagegenderannual_incomecredit_scorecityprovincehousehold_sizeshopping_frequency_monthlyavg_basket_sizemonthly_spendingpreferred_store_typepreferred_payment_methodproduce_spending_pctdairy_spending_pctmeat_spending_pctbakery_spending_pctfrozen_spending_pctpreferred_shopping_daypreferred_shopping_timeloyalty_memberyears_as_customercustomer_satisfactionorganic_preference_pctcoupon_usage_pctonline_shopping_pct
4990CUST_00499118Male25000.00549VancouverBC1952.25440.68SupermarketCredit Card52.812.716.511.96.2SundayMorningFalse4.76.514.96.129.6
4991CUST_00499255Male64537.91657KitchenerON3450.89233.01Convenience StoreCash37.27.532.511.111.8ThursdayAfternoonFalse5.06.529.331.810.5
4992CUST_00499343Female48068.59653TorontoON2759.45360.59SupermarketDebit Card38.85.611.718.725.2ThursdayEveningTrue4.06.925.826.73.0
4993CUST_00499453Male80380.25756KitchenerON41273.77904.23Organic StoreDebit Card44.117.722.26.69.3MondayAfternoonTrue1.68.739.921.61.9
4994CUST_00499535Female124714.47715KitchenerON2699.23502.27SupermarketCredit Card25.327.426.94.515.9TuesdayMorningFalse5.57.844.213.517.5
4995CUST_00499644Male80103.32701CalgaryAB21063.50620.35SupermarketCash26.916.232.78.715.5SundayMorningFalse5.68.618.912.76.3
4996CUST_00499755Female129719.20765KitchenerON21288.451261.24Discount StoreDebit Card38.87.228.511.913.6SaturdayAfternoonTrue0.59.054.313.211.0
4997CUST_00499880Female106375.39825Quebec CityQC2752.18383.73Warehouse ClubDebit Card31.424.74.59.430.0FridayEveningTrue1.48.334.25.614.1
4998CUST_00499957Male64002.28677EdmontonAB1350.29121.78Organic StoreDebit Card42.68.828.010.210.4SaturdayMorningFalse0.38.852.823.40.3
4999CUST_00500032Female63558.32617VancouverBC11134.56429.24SupermarketCredit Card53.217.06.211.612.0TuesdayEveningFalse0.4NaN27.914.91.8